Electronic imaging systems often include a facility for removing the dark floor from a captured image. Image sensors generally exhibit a phenomenon known as dark signal in which an image signal is detected even in the absence of light. The amount of dark signal varies in a random fashion from pixel to pixel in the image sensor, and the dark signal is sensitive to environmental conditions, notably temperature, but the base level of dark signal for a given pixel is reasonably consistent for a given image capture condition. One typical approach to removing the dark signal is to capture a dark frame, an image captured with the shutter closed, in close temporal proximity to the actual image capture (called a contemporary dark frame). This contemporary dark frame is then subtracted on a pixel by pixel basis from the actual image. One shortcoming of this method is that there is a level of noise in the dark signal, so the noise in the dark signal in the image will add (in some fashion related to the noise distribution) to the noise in the dark signal of the dark frame, thereby increasing the noise in the final processed image at the same time the base level dark signal is removed. A further shortcoming is that the dark frame capture should have the same conditions as the actual image capture, notably exposure time. Hence, for a long exposure time in which the dark signal has a long time to accumulate, the dark frame exposure time will have to be equally long, doubling the amount of time required to capture an image.
An alternative method for dark signal removal involves capturing a series of dark frames under some nominal conditions during a calibration process, perhaps at the time the electronic imaging system is manufactured. The series of dark frames is averaged together, thereby significantly reducing the noise component of the dark signal. This averaged dark frame is stored in a non-volatile memory and used as a baseline dark floor. Since the dark signal is sensitive to environmental and image capture conditions, the baseline dark floor would only be useful if the temperature and exposure time of an actual image capture matched the conditions under which the calibrated dark frames were captured. An image sensor generally has light shielded pixels that are used for general offset correction in image processing; the dark pixels from the actual image capture can be compared to the dark pixels from the baseline dark floor, and the result of the comparison can be used to adjust the baseline dark floor to match better the conditions of the actual image capture. This adjusted baseline dark floor is subtracted from the actual image. Although this reduces the additional noise and capture time associated with the previous dark floor removal method, it does have some shortcomings: the dark pixels are not uniformly distributed throughout the image sensor, so regional variations in the temperature of the sensor would not be detected; and some pixels may have a dark signal that behaves abnormally with respect to temperature or exposure time, so the baseline dark floor will not be adjusted correctly for those pixels.